Available for implementation in the beta version since March, 2013, Google Universal Analytics has been proclaimed revolutionary and even more helpful tool to collect data about the users’ behaviour and actions. Therefore, the question every geek has been asking in the past months is: how it is different to the GA that I already know of and how can I make the best use of it? The feature that interests us the most with the new Google Universal Analytics is the introduction of the User ID. This shifts the user ecosystem that we know of to an entirely new level of user-tracking. To understand the importance of the move, we need to take a step back and compare what has been possible with the possibilities we have now. Previously, user-tracking was possible according to the device one was currently using. Thus, GA would identify a person using a smartphone as a user 1 and the person using the laptop as user 2. The ecosystem tracks two different paths even though they should be regarded as one, as the potential client, the user, stays the same. Such an analysis was helpful and insightful, but only in the case when users used only 1 type of device to visit one’s site (fig.1).
But, let’s face it – the number of people owning only one type of device to access the Internet has decreased dramatically over the recent years. Thus, the results drawn from this version of GA may prove inconsistent and discrepant. In order to enable business owners to collect more truthful and accurate data about the clients’ interactions, Google introduced the new way to monitor the behaviour of the users. The User ID allows the business owners to associate multiple sessions on their website and mobile app with a unique ID. This helps to identify web and mobile users who own several devices (fig.2). Cross-Device reports prove increasingly helpful in the analysis of conversion paths and improvement of web, mobile and social media campaigns.
Google Universal Analytics also allows the user to input offline behaviour data, so that one may see how the online traffic affects visits and transactions in-store. However, such an analysis has to be voted to some extent superficial. The offline data also needs to be put in context. It would be incredibly helpful if the store manager could observe how the online content (ads, sponsored posts, online store) influences the number of visits of the particular, identifiable user. This requires taking a step further – implementing ‘online’ and ‘offline’ into one, compatible ecosystem. Here’s how.
Thanks to the new mobile technologies, such as iBeacons of NFC, the brands may introduce their users to customized, personalised messages, offers and deals. Win-win for both parties of the deal. The clients get a better offer in the offline store, the brands may identify the users’ ID and monitor their conversion path in an extremely complex way. Say, you want to see the correlation between the time spent at the particular section in the online store with the time spent at the particular shelf in the offline store? Not a problem while using Google Universal Analytics. Want to know whether the clients who browse your mobile app and opt out from the online purchase come to the offline store and finally buy the product. Could do. This vast ecosystem allows to merge online, mobile and offline into one correlated mechanism (fig.3).